Abstract We present a multi-level delumping method suitable for thermal enhanced oil recovery processes. At low pressures, the temperature variable is the most critical factor impacting the displacement process through viscosity reduction and evaporation/condensation effects. Hydrocarbon components are vaporized under high temperatures, move downstream in the gas phase and condense back to the liquid phase. That process is governed by the K-values of the components, evaporating out of the liquid phase sequentially with increasing temperatures. To reduce the computational cost, it is standard practice to reduce the number of (pseudo-)components used in thermal reservoir simulation. Depending on the number and type of hydrocarbon pseudo-components retained in the simulations, we may not be able to capture the correct displacement due to large errors in the lumped phase behavior (flash) computations. We address that problem through a multi-level method: we use data obtained from a short simulation using the most detailed fluid description available, and leverage that information to guide a delumping process. We use temperature as a proxy variable for composition, and select reference temperatures. We extract the corresponding reference compositions from the detailed run and use them to extend the lumped pseudo-components to an approximate detailed composition. We compute the phase mole fractions as well as the gas compressibility factor. We test our method using six heavy oil samples, and under two different recovery processes: hot nitrogen injection and in-situ combustion (air injection and exothermic oxidation reactions). The average error on the liquid mole fraction is reduced by 4-12 times (depending on the oil samples) compared to the flash using pseudo-components, and the maximum error by 6-48 times. We illustrate that the method is amenable to manually adding more information about the physics of some oil samples. We also discuss how to efficiently pick the reference temperatures. For uniformly sampled temperatures (between a minimum and maximum temperature), we conduct a sensitivity study which led us to use six temperatures. We ran both local (Pattern Search, PS) and global (Particle Swarm Optimization, PSO) gradient-free optimization methods. PS is able to find the closest local minimum to the uniform set, giving a limited improvement of 6.5%. The known increased cost for PSO is worth the investment in at least one of the cases we considered, leading to a 67% improvement.

中文翻译：

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低压下热强化采油模拟的多级分块策略

摘要 我们提出了一种适用于热力强化采油工艺的多级分块方法。在低压下，温度变量是通过粘度降低和蒸发/冷凝效应影响置换过程的最关键因素。烃组分在高温下蒸发，以气相向下游移动并冷凝回液相。该过程由组分的 K 值控制，随着温度的升高从液相中依次蒸发。为了降低计算成本，标准做法是减少热储模拟中使用的（伪）组件的数量。根据模拟中保留的碳氢化合物伪组分的数量和类型，由于集中相位行为（闪光）计算中的大误差，我们可能无法捕获正确的位移。我们通过一种多层次的方法来解决这个问题：我们使用从使用最详细的可用流体描述的简短模拟中获得的数据，并利用该信息来指导分块过程。我们使用温度作为成分的代理变量，并选择参考温度。我们从详细运行中提取相应的参考成分，并使用它们将集总伪分量扩展为近似的详细成分。我们计算相摩尔分数以及气体压缩系数。我们使用六个重油样品并在两种不同的回收过程中测试我们的方法：热氮气喷射和原位燃烧（空气喷射和放热氧化反应）。与使用伪组分闪蒸相比，液体摩尔分数的平均误差降低了 4-12 倍（取决于油样），最大误差降低了 6-48 倍。我们说明该方法适合手动添加有关某些油样物理的更多信息。我们还讨论了如何有效地选择参考温度。对于均匀采样的温度（最低和最高温度之间），我们进行了敏感性研究，这使我们使用了六个温度。我们运行了局部（模式搜索，PS）和全局（粒子群优化，PSO）无梯度优化方法。PS 能够找到最接近统一集的局部最小值，仅提供 6.5% 的有限改进。